Method and system for determining the driving situation

ABSTRACT

A method and a system for ascertaining the driving situation of a motor vehicle by using data provided in the vehicle indicating the value of at least one state variable of the vehicle are provided. To relieve the burden on the driver a data record providing the history of the at least one state variable is supplied. In a neural network in the motor vehicle is supplied by a suitably programmed computer. The neural network has at least one input layer and one output layer, each of the layers having a plurality of perceptrons. The respective value of the at least one state variable of the respective point in time, preferably a normalized value, is supplied to a perceptron of the neural network. After the neural network has been trained, the current driving situation is output by the perceptrons of the output layer of the neural network.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 to InternationalPatent Application No. PCT/EP2004/010704 filed Sep. 22, 2004, and under35 U.S.C. § 119 to German Patent Application No. 103 54 322.8 filed Nov.20, 2003, the entire disclosure of these documents is herein expresslyincorporated by reference.

BACKGROUND AND SUMMARY OF THE INVENTION

The present invention relates to a method for determining the drivingsituation of a motor vehicle and a corresponding system.

Due to the growing volume of information made available in a motorvehicle and the associated demands on the driver, some well-directedrelief is required when the burden is high due to the traffic situation.The present invention provides a method and a system for relieving thedriver.

One aspect of the inventive method is the use of data provided in themotor vehicle, representing the value of at least one state variable ofthe vehicle. This data may be made available via the data bus of thevehicle, for example, for implementation of the inventive method. In afirst step, a data record providing the history of the at least onestate variable is supplied. In a second step, a neural network is madeavailable by a suitably programmed computer in the motor vehicle. Theneural network of the inventive method can have at least one input layerand one output layer, each layer having a plurality of perceptrons. In athird step, the respective value of the at least one state variable ofthe respective point in time, can be standardized to the range of 0 to1, like all the other values, is sent to one perceptron of the neuralnetwork, the current driving situation then being output by theperceptrons of the output layer of the neural network after beingtrained.

A perceptron is a mathematical function (software function) formed bysoftware, calculating from input values an output value that is relayedto various perceptrons. The input values are weighted and the outputvalue of the perceptron is a mapping function of the weighted inputvalues according to the software function.

Exemplary embodiments of the present invention are explained in greaterdetail below.

An exemplary neural network is a sigmoid network that can have threelayers. Each perceptron in this case is formed by the sigmoid function,which is essentially known. Sigmoid networks are advantageouslycharacterized in that the output value of the perceptron, i.e., thesoftware function is largely linear to the output value, whichsimplifies further processing.

The prevailing driving state of the vehicle can be ascertained, i.e.,determined, from a chronological sequence of driving situations thathave been detected. A prevailing driving state is assigned to achronological sequence of driving situations that have been detected onthe basis of at least one assignment specification. Instead of the earlydriving state, a new driving state can be determined only when the newdriving state has already been ascertained, i.e., determined, repeatedlywithin an interval of time that has elapsed.

A different situation can be assigned to each perceptron of the outputlayer and/or its output signal. The maximum output signal of all theoutput signals of the perceptrons of the output layer indicates thecurrent driving situation of the motor vehicle.

The output signal, preferably the signal peak of the first perceptron ofthe output layer of the neural network can indicate a “stop and go”driving situation. The output signal, such as a signal peak, of thesecond perceptron of the output layer of the neural network can bedefined by the “city traffic” driving situation. The output signal, canbe a signal peak of the third perceptron of the output layer of theneural network stands for the “cruising” driving situation. The outputsignal, can be the signal peak, of the fourth perceptron of the outputlayer of the neural network stands for the “sporty” driving situation.In summary, the input layer of the neural network, i.e., thecorresponding computer program in this embodiment of the presentinvention, is chronologically supplied with a data record having statevariables of the motor vehicle and the driving situation is determinedchronologically on the basis of the maximum output signal of allperceptrons of the output layer. It is self-evident that the drivingsituation thereby determined may also be grouped into a larger orsmaller number of classes (“stop and go,” etc.).

In an exemplary embodiment of the present invention, the drivingsituation is considered to be nonspecific when the difference betweenthe value of the maximum output signal of all perceptrons of the outputlayer and the value of the next smaller output signal of the respectiveperceptron is less than 20%. The same thing is also true alternativelyor additionally in another exemplary embodiment when the value of thegreatest output signal of all perceptrons of the output layer is smallerthan 10% of its maximum value. For these optional inventive measures itis possible to take into account only driving situations that have beendetermined with sufficient certainty. This is true in particular ofdetermination of driving state based on the driving situationascertained.

In another exemplary embodiment of the present invention, the extent ofthe information to be relayed to the driver of the vehicle is based onthe driving state ascertained. With the inventive method and/or system,information with a high deflection such as a telephone call can bewithheld from the driver temporarily during a driving state which makeshigh demands on the driver, e.g., rapid driving on the autobahn, i.e.,highway, and/or it is saved for later display, e.g., in a less demandingdriving situation.

In an inventive embodiment and/or an inventive man-machine interface,the driver is able to select which information and/or which totality ofinformation in a driving situation of the first class such as “stop andgo” is to be displayed and/or output for him in a second class, e.g.,“city driving,” etc.

Other objects, advantages and novel features of the present inventionwill become apparent from the following detailed description of theinvention when considered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The present invention is explained in greater detail below withreference to figures, in which

FIG. 1 shows a flow chart to illustrate the inventive method fordetermining the driving situation and the driving state of a motorvehicle; and

FIG. 2. shows the state machine from FIG. 1 in detailed form.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

For future vehicle generations, it is important for the use of the MMIsystems (MMI=man-machine interface) and the vehicle electronics to beadjusted adaptively to the needs of the driver. This may mean, forexample, that the quantity of information supplied is reduced in the MMIof the vehicle in dynamic driving situations (e.g., sporty driving orautobahn driving), and more information may be made available to thedriver in relaxed or even stationary driving situations (e.g., stop andgo).

An important factor here is the driving situation such as fast,concentrated driving on a high-speed road or autobahn, searching drivingin an inner city area or relaxed “cruising” on a rural road. Thisdriving situation is to be recognized by a software method—preferably onthe basis of data already available in the vehicle. Ideally it should bepossible to ascertain the driving situation with data available in thevehicle anyway, e.g., data, messages and/or telegrams on the CAN bus ofthe vehicle. One problem here is that the driving situation may beperceived very subjectively. Furthermore, the driving situation cannotbe determined as a point on the basis of a single point in time, butinstead a period of time of a certain length in the past must be takeninto account. One prerequisite for this is autonomous detection of thedriving situation by the vehicle itself.

The inventive method is executed on an inventive software system whichrecognizes the prevailing driving situation on the basis of data madeavailable anyway via the electronic vehicle bus system (e.g., CAN bus).The optional exclusive use of data available anyway has the advantagethat no additional sensors or control units are required—even a GPSsystem is not absolutely necessary.

-   -   the “stop and go” driving situation is characterized by        stagnation, i.e., a slow flow in traffic at low speeds with        frequent standstills;    -   the “city traffic” driving situation is characterized by fluid        traffic at speeds of approximately 15 km/h up to approximately        70 km/h, frequent steering maneuvers and occasional stopping;    -   the “cruising” driving situation is characterized by relaxed        driving at speeds above 70 km/h, few steering maneuvers and        fluid driving and    -   the “sporty” driving situation is characterized by sporty        driving, higher longitudinal and transverse accelerations and        frequent steering maneuvers.

One aspect of the present invention lies in the “estimate” of thesimilarity of the real driving situation to the driving situationsdefined above because there is virtually never an exact correspondence.Another aspect is that the driving situations are situational modelswhich should be evaluated over a certain period of time to allowreliable detection. If only the current data, in particular the CANdata, at the particular point in time were to be used (e.g., every 500ms), the driving situation might change very 500 ms, which is notdesirable. For example, stopping at a stop light or briefly stoppingwould already be classified and “stop and go,” despite the fact that itis perhaps only a short interruption in the “cruising” drivingsituation. The inventive method and/or system are therefore providedwith a certain “inertia.”

The inventive method and/or system is/are preferably implemented by asoftware solution and/or a programmed sequence control forming a neuralsigmoid network (e.g., a processor executing machine-readable code).After a continuous supply of values describing the state variable of thevehicle, the sigmoid network outputs a classification of the drivingsituation determined on the basis of the values. In other words, theneural network, i.e., the sigmoid network supplies a “similarity value,”which indicates the similarity of the supplied data, i.e., values tostored data, i.e., values responding to the aforementioned drivingsituations and determined as part of training of the sigmoid network inthe aforementioned driving situations. With a high similarity, thedriving situation assigned to the corresponding stored values is assumedto be currently accurate. In this exemplary embodiment, CAN messagesthat conform to a standard from the standardized CAN bus inside thevehicle are used as the input variables into the sigmoid network. TheseCAN messages are preferably chronologically discretized and normalized.Additionally or alternatively, data of another data bus provided in thevehicle may of course also be used to determine the driving situationand/or the driving state, if necessary.

At regular intervals, e.g., intervals of approximately 500 ms toapproximately 2000 ms, a software component picks up defined types ofmessages from the CAN bus and processes them accordingly. In doing so,the following known data telegrams of the CAN bus are analyzed:

-   -   CcarSpeed (speed and longitudinal and transverse acceleration of        the vehicle; CAN identifier: 416 speed)    -   CgearBox (gear engaged and/or gear lever setting; CAN        identifier: 772 status gear)    -   CsteeringWheelAngle (contains position information on the        steering wheel).

It is self-evident that more or fewer data telegrams, i.e., data may beused for calculation of the driving situation and the driving state ifexpedient or necessary.

To permit an easier understanding of the present invention, first theprinciples of the inventive method and a brief outline of a neuralnetwork of the present invention will be given, followed by detailedexplanation of the inventive method and/or system.

The inventive method is roughly divided into two parts.

In the first step the inventive neural network, in particular a sigmoidnetwork, is parametrized. In parameterization, the neural network istrained and the transitions and parameters of the “inertia” of thesystem are adjusted.

In a second step, the instantaneous driving situation and the currentdriving situation in the vehicle are calculated. The second step isdivided as follows:

-   -   pick up of relevant vehicle data from the CAN bus at appropriate        intervals,    -   processing of the data for optimum analysis,    -   calculation of the current driving situation at the        instantaneous point in time on the basis of current and past CAN        data, and    -   calculation of the prevailing driving state.

Neural networks are known methods and/or systems from informatics whichare used, e.g., in image recognition or voice recognition. A neuralnetwork consists of a quantity of so-called perceptrons. A perceptron isa software function having a quantity of input values and calculating anoutput value from them, said output value being relayed as input tovarious perceptrons. The output value of a perceptron is the result ofan imaging function of the weighted input values (inputs) according tothe following function, preferably the sigmoid function:

${F({inputs})} = \frac{1}{1 + {\mathbb{e}}^{\sum\limits_{i \in {Inputs}}i}}$where “inputs” refers to the quantity of weighted input edges.

A neural network consists of n layers, which in turn already consist ofperceptrons, a perceptron of the n-th layer has all the perceptronoutputs of the (n−1)-th layer as input values. A three-layer sigmoidnetwork is preferably used with the inventive method and/or system.

An important aspect of neural networks is that they are “trainable” andcan thus be adapted to the concrete object. In “training,” the networkis adjusted through examples by the known so-called “backpropagation”method so that it classifies the new input values like “trainingvalues.”

In training by the “backpropagation” method, a signal pattern to berecognized is applied to the input layer of the sigmoid network. Theperceptrons, i.e., the corresponding software components, perform acalculation according to the sigmoid function. The result of thecalculation is output in the form of a signal pattern by the outputlayer of the sigmoid network. Parameterization of the weights of theindividual perceptrons is performed by using known learning methods(algorithms) in which examples of input patterns are created andcorresponding output patterns are preselected. The algorithm thenadjusts the weights so that the preselected output patterns arecalculated, i.e., formed for the preselected input patterns.

Example according to the present invention: the input pattern is a setof CAN messages corresponding to the “city traffic” driving situation.The weights are adjusted so that the output pattern of all theperceptrons of the output layer yields signal level 0, only perceptronnumber 2 yields signal level 1. Signal level 1 on perceptron 2 isassumed to be representative for the “city traffic” driving situation.After conclusion of training, the sigmoid network according to thepresent invention classifies real driving situations in a manner similarto that used in the examples of training. It is sufficient here if thereal driving situations and/or the data patterns are similar to thetrained driving situations and/or their data patterns. A completecorrespondence is advantageously not necessary for mostly reliable andcorrect classification.

The inventive method and/or system provided in a vehicle is/aredescribed in greater detail below.

In a first step, the driving situation is determined at regularintervals on the basis of the instantaneous relevant CAN data and acertain data history of this data by the three-layered neural network,preferably a gradient network or a sigmoid network. For example, thecurrent driving situation may be determined every 0.5 second using thespeed and acceleration data at the current point in time t and the dataat points in time t−2 seconds and t−4 seconds.

Then in a second step, the driving state is determined on the basis ofthe current driving situation and the past driving situation asdetected. The present recognized driving situation, the past recognizeddriving situations and the instantaneous driving situation all play arole in determining the driving state.

This will now be illustrated on the basis of an example. If the currentdriving state is “city traffic” and “stop and go” is recognizedrepeatedly as the driving situation, then the driving situation is setat “stop and go,” i.e., this is regarded as a given, only after the“stop and go” driving situation has been recognized, for example, eighttimes in a row. Otherwise, in city traffic, a single traffic stop lightwould result in the driving state being set at “stop and go.” However,if the vehicle is in the “cruising” driving state, then it is preferablehere to set “stop and go” as the driving state after recognizing the“stop and go” driving state, for example, three times in a row.

As shown in the flow chart 100 in FIG. 1, a software component 2 picksup the data required for ascertaining the driving situation at suitableintervals, preferably 0.5 to 2 seconds, from the CAN bus 1 and forms ahistory v(t), v(t−1), v(t−2), etc. of data thus picked up. The data ofthe data history is supplied to the perceptrons of the input layer ofthe three-layer sigmoid network 3 which outputs the calculatedinstantaneous driving situation 4 after parametrization and training ofthe system. The driving situations thereby determined are sent to aso-called state machine 5 which calculates the current driving statefrom the driving situation's output and outputs it on the basis of thefollowing procedures. The different driving states are labeled as A, B,C, and D, for example, in FIG. 1. This is done continuously using theprogressive data history.

The sigmoid network is trained to the driving situations consideredexpedient by the backpropagation and/or gradient descent method. Inother words, a certain driving situation is assigned to certain(typical) input data into the sigmoid network and the weighting in thesigmoid network is adjusted so that the output perceptron that standsfor a certain driving situation corresponding to the respective drivingsituation is assigned a maximum value.

A driving state is assigned to a history of driving situations by thestate machine 5 according to the table in FIG. 2. This assignment, i.e.,table is based on empirical values.

The steps in the computation method for ascertaining the drivingsituation and the driving state are explained in greater detail below onthe basis of exemplary data:

1. Picking up the relative telegrams on the CAN bus in a certaininterval—e.g., every 0.5 to 2 seconds.

2. Processing the CAN data to yield a result vector having theparameters:

-   -   a. Speed    -   b. Steering angle is measured continuously and averaged over the        time frame of the window (t−1, t)    -   c. Transverse acceleration is measured continuously and averaged        over the time frame of the window (t−1, t)    -   d. Longitudinal acceleration is measured continuously and        averaged over the time frame of the window (t−1, t)    -   e. Gear status

Then all data is normalized to have a value between 0 and 1.

3. Input of the vectors into the sigmoid network. Calculation of thecurrent driving situation on the basis of the current and last two datarecords—i.e., an evaluation based on speed, steering performance andshifting in the last 6 seconds. The following outputs of the outputlayer of the sigmoid network are processed:

-   -   a. Perceptron 0 is at maximum: driving situation=stop and go    -   b. Perceptron 1 is at maximum: driving situation=city traffic    -   c. Perceptron 2 is at maximum: driving situation=cruising    -   d. Perceptron 3 is at maximum: driving situation=sporty    -   e. If the difference between the first maximum and the second        maximum is <0.2 or the maximum is <0.1, the driving situation is        considered as being undefined.

4. The current driving situation which is calculated every two secondsdetermines the driving state as follows:

-   -   f. If the system is in driving state A at the point in time t,        then driving situation B is recognized and driving situation C        is recognized at point in time t−1, then the following holds:        -   i. C=undefined, i.e., the current driving state is retained        -   ii. The value assigned to driving situation B is greater            than or equal to the value assigned to driving situation C:            a counter reading Z is incremented by one. The value of the            respective driving situation is defined as “stop and go”=0,            “city traffic”=1, “cruising”=2, “sporty”=3.    -   g. If there is a change in driving state, the counter reading Z        is set at 0. If the same driving situation is recognized        repeatedly, the driving state determined changes. This        accomplished by a state machine taking into account the counter        reading in the way indicated in the table in FIG. 2. In other        words, for example:    -   if the “sporty” driving state is recognized four times in a row        in succession and/or continuously for 8 seconds and if the        current driving state is still the “stop and go” driving state,        then the “sporty” driving state is considered as being the        currently prevailing state    -   if the “cruising” driving state is recognized eight times in a        row in succession and/or continuously for 18 seconds and if the        current driving state is still the “city traffic” driving state,        then the “cruising” driving state is considered as being the        currently prevailing state.

This method may be employed with various parameters (history, CAN data,etc.). Therefore, a general description of the method will be presentedhere again.

This method depends on the amount of n input parameters, the width ofthe time window δ and the length of the history included, k·δ. Thismethod is thus roughly divided into the following steps:

1. Picking up, discretizing and processing the s input parameters:

-   -   a. Picking up the input parameters at point in time t    -   b. Picking up the input parameters at point in time t+1=t+δ    -   c. For many types of signals that fluctuate between time cycles        t and t+1, it is beneficial to average the values. If the signal        p in the time window (t, t+1) is output on the bus n times, then        the calculation is performed as follows    -   d. Repeating step 2 k times, yielding an input vector of the        following form at time t0    -   e. Normalizing the input parameters so that all values are        between 0 and 1    -   f. Any numerical differentiation according to t selected signals        increases the recognition output.

2. Calculating the input vector with the help of the three-layer sigmoidnetwork:

An input signal vector of the defined form from 1.d is fed in discreteintervals δ into the sigmoid network. The data is shifted here throughthe input vector (“sliding window”).

-   -   a. A three-layer sigmoid network is used for the calculation,        having an input perceptron for each input time value, i.e., k·n        input perceptrons).    -   b. Instead of the numerical differentiation from point 1.d, a        second hidden layer may also be used in the network.    -   c. The number of output perceptrons is equal to the number of        driving situations to be recognized.

3. Calculation of the driving situation:

-   -   a. Perceptron k is at maximum: driving situation k is considered        as given.    -   b. If the difference between the first maximum and the second        maximum is <0.2 or the maximum is <0.1, then “undefined” is        output as the driving situation recognized.

4. Analysis and time delay due to state machine:

The current driving situation, which is calculated periodically withregard to δ, now has the following influence on the driving stateascertained.

-   -   a. If the system at point in time t is in driving state A,        driving situation B is recognized and driving situation C was        recognized at point in time t−1, then the following holds:        -   i. C=undefined; the current driving state is continued        -   ii. The value of driving situation B is greater than or            equal to the value of driving situation C: the counter            reading Z is incremented by one. An order of driving            situations to be defined is applicable here, i.e., values            such as 0, 1, 2, 3, and 4 are assigned to the driving            situations.    -   b. If the same driving situation has been recognized repeatedly,        the driving state changes. This is accomplished by the state        machine by taking counter reading Z into account accordingly        (see above).    -   c. If Z reaches the defined value, the driving state changes and        Z is set at zero.

5. The method begins again at step 1.

It is clear from the preceding discussion that the inventive methodand/or system permit(s) a reliable determination of the drivingsituation and the driving state. In other words, the actual driving modeis used as input, not the trip environment (e.g., “highway”) and thedriving mode presumed to be associated with it. Systems based onposition determination (when “on the highway” is determined (via GPS),then the driving situation is considered as being “driving fast”) areless accurate because the actual driving mode may be quite different.With the method and system described here, it is possible to determinethe driving situation regardless of the driving environment. Forexample, if the driver exhibits a driving performance on a country roadlike that on the highway, the “highway” driving situation will berecognized regardless of the actual conditions.

The inventive method and/or system may be advantageously—but need notbe—implemented on the basis of the standard CAN data present in thevehicle anyway or other data that is inexpensively available in anyvehicle without requiring special equipment such as a navigation systemwith GPS.

The inventive method and/or system is easily parametrizable and can thusbe adapted flexibly to altered boundary conditions (e.g., new type ofvehicle). Furthermore, the hardware requirements are very low (CPU,memory use) and no additional sensors are required. Therefore, it can bemade available to drivers at a low cost from this standpoint as well.

The inventive method and/or system for recognition of the drivingsituation and/or driving state may be used for adaptation of anysuitably adaptable vehicle functions and/or vehicle systems depending onthe driving situation and/or driving state. Examples include:

-   -   adaptive mission control    -   adaptive running gear    -   adaptive cruise control    -   adjustment of the scale of the navigation map on the display of        an electronic navigation system, depending on the driving        situation detected    -   adaptive steering translation in active front steering    -   adaptive light width regulation and/or adaptive light cone        adjustment.

Another possible application is operation of man-machine interfaces as afunction of driving situation and/or driving state to relieve the burdenon the driver; for example, the quantity of information forwarded to thedriver may be based on the current driving state. Of course there aremany other applications that take into account the current drivingstate.

The foregoing disclosure has been set forth merely to illustrate theinvention and is not intended to be limiting. Since modifications of thedisclosed embodiments incorporating the spirit and substance of theinvention may occur to persons skilled in the art, the invention shouldbe construed to include everything within the scope of the appendedclaims and equivalents thereof.

1. A processor programmed to execute machine-readable code to performthe acts of: receiving a data record of a history of the at least onestate variable; generating, by a programmed computer, a neural networkin the motor vehicle, the neural network having at least one input layerand one output layer, each of the layers having a plurality ofperceptrons; supplying, to a perceptron of the neural network, arespective value of the at least one state variable of a respectivepoint in time; and outputting the current driving situation by theperceptrons of the output layer of the neural network after it has beentrained, wherein a current driving state of the vehicle is set and/ordefined from a chronological sequence of driving situations ascertained.2. A method for ascertaining a driving situation of a motor vehicle byusing data provided in the vehicle indicating the value of at least onestate variable of the vehicle, the method comprising the acts of:receiving a data record of a history of the at least one state variable;generating, by a programmed computer, a neural network in the motorvehicle, the neural network having at least one input layer and oneoutput layer, each of the layers having a plurality of perceptrons;supplying, to a perceptron of the neural network, a respective value ofthe at least one state variable of a respective point in time; andoutputting the current driving situation by the perceptrons of theoutput layer of the neural network after it has been trained, wherein acurrent driving state of the vehicle is set and/or defined from achronological sequence of driving situations ascertained.
 3. The methodof claim 2, wherein instead of an earlier driving state, a new drivingstate is ascertained only when the new driving state has been set and/ordetermined repeatedly within an interval of time that has elapsed. 4.The method of claim 2, wherein at least one device of the vehicle isadapted to the driving situation ascertained, the driving stateascertained, or the extent of the information forwarded to the driver ofthe motor vehicle is based on the driving state ascertained.
 5. Themethod of claim 2, wherein the respective value of the at least onestate variable of the respective point in time is a normalized value. 6.The method of claim 2, wherein the neural network is a sigmoid network.7. The method of claim 6, wherein the neural network has three layers.8. A method for ascertaining a driving situation of a motor vehicle byusing data provided in the vehicle indicating the value of at least onestate variable of the vehicle, the method comprising the acts of:receiving a data record of a history of the at least one state variable;generating, by a programmed computer, a neural network in the motorvehicle, the neural network having at least one input layer and oneoutput layer, each of the layers having a plurality of perceptrons;supplying, to a perceptron of the neural network, a respective value ofthe at least one state variable of a respective point in time; andoutputting the current driving situation by the perceptrons of theoutput layer of the neural network after it has been trained, wherein acurrent driving state is assigned to a chronological sequence of drivingsituations ascertained, this assignment being made on the basis of atleast one allocation procedure.
 9. The method of claim 8, wherein atleast one device of the vehicle is adapted to the driving situationascertained, the driving state ascertained, or the extent of theinformation forwarded to the driver of the motor vehicle is based on thedriving state ascertained.
 10. The method of claim 8, wherein therespective value of the at least one state variable of the respectivepoint in time is a normalized value.
 11. The method of claim 8, whereinthe neural network is a sigmoid network.
 12. The method of claim 11,wherein the neural network has three layers.
 13. A method forascertaining a driving situation of a motor vehicle by using dataprovided in the vehicle indicating the value of at least one statevariable of the vehicle, the method comprising the acts of: receiving adata record of a history of the at least one state variable; generating,by a programmed computer, a neural network in the motor vehicle, theneural network having at least one input layer and one output layer,each of the layers having a plurality of perceptrons; supplying, to aperceptron of the neural network, a respective value of the at least onestate variable of a respective point in time; and outputting the currentdriving situation by the perceptrons of the output layer of the neuralnetwork after it has been trained, wherein a different driving situationis assigned to each perceptron of the output layer or its output signaland the maximum output signal of all output signals of the perceptronsof the output layer indicates the current situation of the motorvehicle.
 14. The method of claim 13, wherein the driving situation isconsidered indefinite if the difference between the value of the maximumoutput signal of all perceptrons of the output layer and the value of anext smaller output signal of the respective perceptron is less than 20%or the value of the greatest output signal of all perceptrons of theoutput layer is less than 10% of its maximum value.
 15. The method ofclaim 13, wherein the output signals of the perceptrons arestandardized.
 16. The method of claim 13, wherein at least one device ofthe vehicle is adapted to the driving situation ascertained, the drivingstate ascertained, or the extent of the information forwarded to thedriver of the motor vehicle is based on the driving state ascertained.17. The method of claim 13, wherein the respective value of the at leastone state variable of the respective point in time is a normalizedvalue.
 18. The method of claim 13, wherein the output signal of thefirst perceptron of the output layer of the neural network indicates a“stop and go”, of the second perceptron of the output layer of theneural network indicates a “city driving” driving situation, of thethird perceptron of the output layer of the neural network indicates a“cruising” driving situation, or of the fourth perceptron of the outputlayer of the neural network indicates a “sporty” driving situation. 19.The method of claim 18, wherein the output signal is a signal maximum.20. The method of claim 13, wherein the neural network is a sigmoidnetwork.
 21. The method of claim 20, wherein the neural network hasthree layers.